GPU as a Service - H Series

A GPU (Graphics Processing Unit) is a specialized processor designed to execute graphic and complex computational tasks through parallel processing. In Artificial Intelligence (AI) and Machine Learning (ML) projects, GPUs deliver significantly higher speed and performance compared to CPUs. Specifically, for Large Language Models (LLM), they accelerate projects by executing massive mathematical functions—such as matrix multiplications—in seconds through their parallel processing capabilities.

GlassHouse GPU as a Service (GPUaaS) is a high-availability, KVKK-compliant cloud GPU infrastructure hosted entirely within data centers in Turkey. It provides enterprises with access to the latest NVIDIA H100 architecture via a OPEX model, eliminating the need for heavy initial hardware investment (CAPEX).

The NVIDIA H100 is built on the next-generation Hopper architecture and delivers 2x to 7x faster performance compared to the previous-generation A100. With its 80 GB HBM3 (High Bandwidth Memory) capacity, it prevents bottlenecks by processing massive datasets at terabytes-per-second speeds, providing unparalleled power for LLM training and inference.

MIG (Multi-Instance GPU) is a technology that allows a single physical NVIDIA H100 GPU to be partitioned into 7 independent instances isolated at the hardware level. Unlike time-shared vGPU solutions, MIG assigns fully dedicated and isolated resources to each workload, eliminating the "noisy neighbor" problem and offering guaranteed performance.

VRAM (Video RAM) is the GPU’s fast, dedicated memory used to store data for instantaneous parallel calculations. The larger the LLM (in terms of parameter count), the more VRAM it requires. If VRAM is insufficient, the model either fails to run or reverts to the much slower system RAM, resulting in significant performance loss.

Model Training: Refers to the "post-production" process of training a model for weeks using billions of data points, requiring the highest computational power.

Inference: The real-time generation of instantaneous responses from trained models (e.g., ChatGPT-like chatbots), where low latency is critical. GlassHouse GPUaaS offers a scalable, high-performance infrastructure for both workload types.

Yes. The entire GlassHouse GPU as a Service infrastructure is hosted in Tier III data centers located within Turkey.

The Managed GPU service provided by Glasshouse goes beyond providing simple hardware access, offering end-to-end support that encompasses all operational and technical layers of the GPU ecosystem. According to the service framework, the core technical details are as follows:

GPU Ecosystem and Component Management
The most critical part of the managed service is the management of the software layers required for the GPU to operate efficiently.

Software Stack Installation and Patching: Installation, management, and updating of core GPU ecosystem components such as CUDA, NVIDIA Container, and Docker are carried out by Glasshouse experts.

Periodic Updates (Patch Management): Patches for the Operating System, GPU hardware, and driver requirements are applied regularly in 3-month cycles in coordination with the customer.

NVIDIA Compatibility: OS updates supported by NVIDIA or the GPU manufacturer are continuously monitored and integrated into the system.

Operating System (OS) and Infrastructure Management
The Managed GPU service also covers the operating system layer running on the hardware (Managed OS).

Installation and Configuration: This includes the installation of the OS, performing baseline configurations, and installing specialized drivers according to customer requirements.

File System and Disk Management: Directory management, intervention in file system failures, and monitoring of disk utilization rates are provided.

Access and Security: Access controls on the server, fulfillment of requests regarding security vulnerabilities, and meticulous tracking of system logs are maintained.

24/7 Monitoring and Operational Intervention
The service is supported by advanced monitoring mechanisms to guarantee business continuity.

Advanced Monitoring: Beyond standard infrastructure, specific metrics such as GPU VRAM utilization and Power State are monitored 24/7.

Incident Management: In case of access disruptions or server/network issues, immediate intervention is provided in accordance with defined Service Level Agreements (SLAs).

Service Ownership: A "service ownership matrix" is created by identifying the services running on the server to ensure all operational gaps are closed.

Strategic Advantages
This technical scope provides enterprises with the fundamental benefit of "focusing on innovation by eliminating operational burden." Instead of dealing with CUDA version incompatibilities or driver errors in complex LLM (Large Language Models) and AI projects, organizations can accelerate their initiatives by leveraging Glasshouse's ITIL-standard management model.

A GPU alone is not a complete solution; the data feeding layers (CPU, RAM, storage, and network) must be powerful enough to support it. If the dataset originates from slow storage, the H100—capable of processing terabytes per second—will remain "idle," extending project timelines. GlassHouse provides high-performance NVMe SSD storage and 100 Gbps network infrastructure to prevent these bottlenecks.

In LLMs, the total model size and the number of active parameters used in a specific calculation can differ. Architectures like MoE allow for more optimized VRAM usage, enabling massive models (e.g., those occupying 800 GB of disk space) to run with appropriate VRAM configurations.

RAG structures working on corporate document sets or Agentic AI systems providing intelligent responses can produce high-accuracy results with low latency without requiring massive VRAM capacities. These methods reduce costs by utilizing GPU parallel processing power most efficiently.

Purchasing GPUs requires high CAPEX (Capital Expenditure). With GlassHouse’s model, enterprises manage these costs as OPEX (Operational Expenditure). This eliminates hidden costs such as hardware depreciation, energy, cooling, and maintenance, allowing budgets to be redirected toward innovation.

No. Thanks to the 4th Gen NVLink technology in the NVIDIA H100, up to 8 GPUs can be interconnected with a massive bandwidth of 900 GB/s. This "Distributed Model Training" capability ensures linear scaling without performance loss during LLM training with large datasets.

Our service is designed for all sectors requiring high-volume Natural Language Processing (NLP), large-scale Computer Vision, Deep Learning, and Big Data Analytics. It is widely used in industries where data security is critical, such as banking, payment systems, and healthcare.

GlassHouse infrastructure is geographically redundant and monitored 24/7. In the event of a hardware failure, workloads are automatically transferred to healthy GPU resources through N+1 redundancy structures or Kubernetes orchestration, minimizing operational downtime.

Yes. GlassHouse GPUaaS infrastructure is hosted entirely in data centers within Turkey. This physical locality fully meets the "data residency" and "isolated hosting" requirements of regulators such as BDDK (Banking Regulation and Supervision Agency) and SPK (Capital Markets Board).

A powerful GPU will remain "idle" and wait if supported by slow storage. To prevent such bottlenecks, GlassHouse supports its GPU units with high-performance NVMe SSD areas and 100 Gbps low-latency network switches, ensuring that data feeding speeds are synchronized with the GPU's processing power.